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We develop methodologies and apply machine-learning algorithms to a database of ALS patients to expose and model underlying mechanisms and relations in the disease. We view the disease state as an ordinal variable (with values between 4 for normal function and 0 for complete loss of function), and show that ordinal classification applied to the data has an advantage over classification that does not...
The task of text/non-text stroke classification in online handwritten documents is an essential preprocessing step in document analysis. It is also a challenging problem since in many cases local features are not enough to generate high accuracy results and contextual information, such as temporal information and spatial information, must be carefully considered. In this paper, we propose a novel...
This paper deals with automatic image colorization. This is a very difficult task, since it is an ill-posed problem that usually requires user intervention to achieve high quality. A fully automatic approach is proposed that is able to produce realistic colorization of an input grayscale image. Motivated by the recent success of deep learning techniques in image processing, we propose a feed-forward,...
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